Guided VAE [1]} also gives out a model using different strategies in supervised and unsupervised situations to get disentanglement representation. It uses additional discriminator to guide the representation learning and learn the knowledge about latent geometric transformation and principal components. This idea of using different methods with different supervised information inspires us. FineGAN [2]} based on InfoGAN, generates the background, object shape, and object appearance images respectively in different hierarchies, then combines these three images into true image. In FineGAN, what helps the disentanglement is the mutual information between the latent codes and each factor. And MixNMatch [3]}, developed from FineGAN, becomes a conditional generative model that learns disentangled representation and encodes different features from real image and then uses additional discriminators to match the representation to the prior distribution given by FineGAN model.
